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DeepSeek launches DeepSeek v3.2 experiment with high efficiency

Published

10 months ago

on

DeepSeek

DeepSeek has released a new DeepSeek v3.2, which is an experimental model that delivers high efficiency over the previous model.

This model has achieved fine-grained sparse attention with minimized impact on output quality, improving long-context performance and reducing compute cost.

It is built on DeepSeek V3.1-Terminus, and the inference cost of the new experimental model is way lower than that of V3.2-Experimental. On the other hand, the benchmarks of this new experimental build are on par with V3.1 Terminus.

DeepSeek v3.2 Benchmarks

The V3.2 Experimental API is now available with $0.028 cach hit and $0.28 cache miss for input tokens and $0.42 on output. New prices are effective immediately from today.

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Mannoo specializes in Generative AI, Large Language Model (LLM), and Aerospace Science. Prior to delving into these fields, he was a Python programmer, a game designer, and an Android and iOS app developer with over 5 years of experience. He has prior writing experience in creative writing about smartphones and technology before working at Eonmsk.com. You can explore his X/TWitter and LinkedIn pages or contact him through his email.

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